Evolutionary-Based Support Vector Machine

被引：0

作者：

Kuo, R. J. ^{[1
]}

Chen, C. M. ^{[2
]}

机构：

[1] Natl Taiwan Univ Sci & Technol, Dept Ind Management, Taipei, Taiwan

[2] Natl Taipei Univ Technol, Dept Ind Engn & Management, Taipei, Taiwan

来源：

2011 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM) | 2011年

关键词：

Support vector machine; evolutionary algorithms; particle swarm optimization; artificial immune sytem; FEATURE-SELECTION; OPTIMIZATION;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This study proposed a hybrid of artificial immune system (AIS) and particle swarm optimization (PSO)-based support vector machine (SVM) (HIP-SVM) for optimizing SVM parameters. In order to evaluate the proposed HIP-SVM's capability, six benchmark data sets, Australian, Heart disease, Iris, Ionosphere, Sonar and Vowel, were employed. The computational results showed that HIP-SVM has better performance than AIS-based SVM and PSO-based SVM.

引用

页码：472 / 475

页数：4

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